scholarly journals Information technology for developing a knowledge base for automated solution of problems in the process of planning and organization of an air strike on anyone’s objects

Connectivity ◽  
2020 ◽  
Vol 146 (4) ◽  
Author(s):  
A. V. Samokish ◽  
◽  
Ye. S. Vorobyov ◽  
P. G. Berdnik ◽  
◽  
...  
Keyword(s):  
Information Technology ◽  
Knowledge Base ◽  
Fuzzy Neural Network ◽  
Subject Area ◽  
Optimal Plan ◽  
Modeling Methodology ◽  
Full Control ◽  
Formal Presentation ◽  
Ground Target ◽  
Fuzzy Neural

The article proposes an information technology for the development of a knowledge base for the automated solution of problems in the planning and organization of an air strike against enemy targets, using fuzzy sets and fuzzy logic. A formal presentation of information technology was performed using the IDEF0 functional modeling methodology. The use of a single hardware and software platform in the form of a set of standard platforms and unique architectural layers corresponding to the studied subject area will provide full control over the fuzzy neural network and achieve their benefits through the use of training and will increase the efficiency and validity of decision making. In the process of assessing the situation and making a decision, the commander seeks to present (predict) the dynamics of the future battle. This is necessary in order to develop an optimal plan of action, a reasonable allocation of forces and resources, as well as to determine measures for the rational implementation of the combat mission. The difficulties associated with planning the actions of aviation units for various tactical purposes are most evident in the operations of inflicting damage on enemy ground targets. The task of defeating which is one of the main for strike aircraft. The ground target is an extremely complex and multifunctional dynamic system that combines objects that solve various functional tasks. Planning of actions of aviation forces and means when striking ground targets requires the development of an optimal (in terms of the accepted criterion of optimality) scenario of their application, which provides for spatial and temporal coordination of aircraft from the aviation group. The most complete such coordination can be ensured through the use of a set of models that most adequately describe the space-time state of all aircraft, conditions and results of the use of aircraft.

GA-Based Fuzzy Neural Network for Adaptive Fuzzy Modeling and Control

1998 ◽  
Author(s):  
Tongjian Chen ◽  
Yonghong Peng ◽  
Weiqiang Xie ◽  
Hongming Deng
Keyword(s):  
Neural Network ◽  
Control System ◽  
Fuzzy Control ◽  
Knowledge Base ◽  
Fuzzy Neural Network ◽  
Adaptive Fuzzy Control ◽  
Fuzzy Reasoning ◽  
Reasoning Process ◽  
Adaptive Fuzzy ◽  
Fuzzy Neural

Abstract Fuzzy logic theory has provided a model-free tool to develop intelligent control system for complex industrial processes by means of simulating the fuzzy reasoning process of human being. However, the performance of such a control system depends on the knowledge base (control rules and membership functions of fuzzy sets). For the control of complex industrial process in which the dynamic parameters of process is time-varying and non-linear, it is necessary to modify and optimize the knowledge base on-line. Adaptive fuzzy control provides a efficient approach for this objective. In this paper, a new fuzzy neural network (FNN) and an adaptive learning mechanism based on genetic algorithm has been proposed for modeling the fuzzy reasoning process and constructing an efficient adaptive fuzzy control systems. Experiment results show that the FNN is capable of modeling complex functions and simulating fuzzy reasoning process of human being.

A fuzzy neural network approach for modeling the growth kinetics of FeB and Fe2B layers during the boronizing process

2018 ◽  
Vol 106 (6) ◽  
pp. 603 ◽  
Author(s):  
Bendaoud Mebarek ◽  
Mourad Keddam
Keyword(s):  
Neural Network ◽  
Experimental Data ◽  
Fuzzy Neural Network ◽  
Treatment Time ◽  
Calculation Model ◽  
Average Error ◽  
Network Approach ◽  
Neural Network Approach ◽  
Fuzzy Neural ◽  
Kinetics Of

In this paper, we develop a boronizing process simulation model based on fuzzy neural network (FNN) approach for estimating the thickness of the FeB and Fe2B layers. The model represents a synthesis of two artificial intelligence techniques; the fuzzy logic and the neural network. Characteristics of the fuzzy neural network approach for the modelling of boronizing process are presented in this study. In order to validate the results of our calculation model, we have used the learning base of experimental data of the powder-pack boronizing of Fe-15Cr alloy in the temperature range from 800 to 1050 °C and for a treatment time ranging from 0.5 to 12 h. The obtained results show that it is possible to estimate the influence of different process parameters. Comparing the results obtained by the artificial neural network to experimental data, the average error generated from the fuzzy neural network was 3% for the FeB layer and 3.5% for the Fe2B layer. The results obtained from the fuzzy neural network approach are in agreement with the experimental data. Finally, the utilization of fuzzy neural network approach is well adapted for the boronizing kinetics of Fe-15Cr alloy.

Inverse Control of Cable-driven Parallel Mechanism Using Type-2 Fuzzy Neural Network

2010 ◽  
Vol 36 (3) ◽  
pp. 459-464 ◽  
Author(s):  
Cheng-Dong LI ◽  
Jian-Qiang YI ◽  
Yi YU ◽  
Dong-Bin ZHAO
Keyword(s):  
Neural Network ◽  
Parallel Mechanism ◽  
Fuzzy Neural Network ◽  
Inverse Control ◽  
Fuzzy Neural

Research of Fault Diagnosis of Belt Conveyor Based on Fuzzy Neural Network

2014 ◽  
Vol 8 (1) ◽  
pp. 916-921
Author(s):  
Yuan Yuan ◽  
Wenjun Meng ◽  
Xiaoxia Sun
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Bp Neural Network ◽  
Fault Location ◽  
Fuzzy Neural Network ◽  
Fuzzy Theory ◽  
Belt Conveyor ◽  
Fuzzy Neural ◽  
Neural Network Algorithm ◽  
Network Fault

To address deficiencies in the process of fault diagnosis of belt conveyor, this study uses a BP neural network algorithm combined with fuzzy theory to provide an intelligent fault diagnosis method for belt conveyor and to establish a BP neural network fault diagnosis model with a predictive function. Matlab is used to simulate the fuzzy BP neural network fault diagnosis of the belt conveyor. Results show that the fuzzy neural network can filter out unnecessary information; save time and space; and improve the fault diagnosis recognition, classification, and fault location capabilities of belt conveyor. The proposed model has high practical value for engineering.

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